Benchmarking learned algorithms for computed tomography image reconstruction tasks

Applied Mathematics for Modern Challenges, vol. 3, pp. 1–43

Abstract

Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image reconstruction. However, the lack of large-scale, open-access datasets has hindered the comparison of different types of learned methods. To address this gap, we use the 2DeteCT dataset, a real-world experimental computed tomography dataset, for benchmarking machine learning based CT image reconstruction algorithms. We categorize these methods into post-processing methods, learned/unrolled iterative methods, learned regularizer methods, and plug-and-play methods, and provide a pipeline for easy implementation and evaluation. Using key performance metrics, including SSIM and PSNR, our benchmarking results showcase the effectiveness of various algorithms on tasks such as full data reconstruction, limited-angle reconstruction, sparse-angle reconstruction, low-dose reconstruction, and beam-hardening corrected reconstruction. With this benchmarking study, we provide an evaluation of a range of algorithms representative for different categories of learned reconstruction methods on a recently published dataset of real-world experimental CT measurements. The reproducible setup of methods and CT image reconstruction tasks in an open-source toolbox enables straightforward addition and comparison of new methods later on. The toolbox also provides the option to load the 2DeteCT dataset differently for extensions to other problems and different CT reconstruction tasks.

Authors 7

1. Maximilian B. Kiss corresponding
2. Ander Biguri

   University of Cambridge

   Affiliation as printed

   Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom

3. Zakhar Shumaylov

   University of Cambridge

   Affiliation as printed

   Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom

4. Ferdia Sherry

   University of Cambridge

   Affiliation as printed

   Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom

5. K. Joost Batenburg Aachen Leiden Institute for Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute for Advanced Computer Science, Leiden University, The Netherlands

6. Carola‐Bibiane Schönlieb

   University of Cambridge

   Affiliation as printed

   Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom

7. Felix F. Lucka

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging, Centrum Wiskunde & Informatica, The Netherlands